Advances for women in radiology, and AI and radiomics are in the headlines.

This week’s articles in Diagnostic Reading include: female representation in radiology; the mechanics of radiomics; radiologists’ tips for using social media; the relationship between radiology residents’ workload and their clinical performance; and an AI algorithm that could predict successful medication response.

Though female representation on radiology journal editorial boards remains relatively low, researchers recently wrote in Academic Radiology that progress is improving. More women are entering academic radiology early in their careers, and JACR’s first female editor-in-chief will take the journal’s reigns in January 2019. Read the blog by a young woman pursuing her medical imaging degree in Australia.

Radiomics—which consists of extracting large sets of complex descriptors from clinical images without any prior hypothesis—has the potential to generate new hypotheses and patient profiles, and probably to discover new genes, according to a prominent French researcher. Confirming or infirming hypotheses has long driven scientific research, however, this traditional and costly approach is now giving way to data-driven initiatives.

In this article, radiologists share their tips for getting the most out of social media. In addition to using Twitter as an information-gathering tool, radiologists can join in the conversation and collaborate with colleagues around the world. Twitter and other social media platforms also can be used for education as well as to promote the accomplishments of radiology departments.

Radiology residents tend to see their clinical and test performances improve as they get more experience interpreting studies. If residents are asked to do too much, however, their performance will suffer. Researchers studied this delicate relationship between volume and clinical performance and shared their findings in Academic Radiology. The authors also noted previous studies have found increased volumes can lead to higher discrepancy rates for radiologists, but this does not mean programs should go easy on trainees. Read the blog by Dr. Flemming to learn how mindfulness meditation helps him stay focused and precise while keeping pace with the growing number of readings.

A new artificial intelligence (AI) algorithm could help predict whether a patient will successfully respond to medication for a mood disorder. According to recently published research in Acta Psychiatrica Scandinavica, this AI algorithm can analyze brain scans to better classify mood disorders and predict medication response in patients. The research also may suggest that biomarkers may help distinguish certain mood disorders from others, such as major depressive disorder (MDD) and bipolar disorder.

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